Authors
Marius Kloft, Pavel Laskov
Publication date
2007
Journal
NIPS Workshop on Machine Learning in Adversarial Environments for Computer Security
Volume
19
Description
Introduction. Online anomaly detection techniques are steadily gaining attention in the security community, as the need grows to identify novel exploits in highly non-stationary data streams. The primary goal of online anomaly detection is to dynamically adjust the concept of normality while still detecting anomalous behavior. Online anomaly detection can be seen at a high level of abstraction as a following process:
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Scholar articles
M Kloft, P Laskov - NIPS Workshop on Machine Learning in Adversarial …, 2007